Cocoa, livelihoods, and deforestation within the Tridom landscape in the Congo Basin: A spatial analysis

In the context of emerging international trade regulations on deforestation-free commodities, the drivers of households’ deforestation in conservation landscapes are of interest. The role of households’ livelihood strategies including cocoa production, and the effects of human-elephant conflict are investigated. Using a unique dataset from a survey of 1035 households in the Tridom landscape in the Congo basin, the spatial autoregressive model shows that: (1) Households imitate the deforestation decisions of their neighbors; (2) A marginally higher income from cocoa production-based livelihood portfolios is associated with six to seven times higher deforestation compared to other livelihood strategies with a significant spillover effect on neighboring households’ deforestation. The increase in income, mainly from cocoa production-based livelihoods in open-access systems can have a negative effect on forests. Households with a higher share of auto-consumption are associated with lower deforestation. If economic development brings better market access and lower auto-consumption shares, this is likely to positively influence deforestation. Without proper land use planning/zoning associated with incentives, promoting sustainable agriculture, such as complex cocoa agroforestry systems, may lead to forest degradation and deforestation.

Global Challenges Research Fund (UKRI GCRF) through the Trade, Development and the Environment Hub project (project number ES/S008160/1).The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.https://gtr.ukri.org/projects?ref=ES%2FS008160%2F1 3 The UMR BETA is supported by a grant overseen by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program (ANR-11-LABX-0002-01, Lab of Excellence ARBRE).The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.https://anr.fr/en/Answer: This comment is addressed in the manuscript in three steps.The first two steps were in the manuscript initially submitted the third is now added as a complementary answer to the comment.
"After estimating an SLM, we first tested for the existence of spatial autocorrelation using the "Moran i" statistic on the residual of the linear model.Further, we proceeded to the Lagrange Multiplier test which helps to find the type of spatial effects that fit with our data generation process.Tables 2 and 3 in subsection 5.1 display our procedure of model specification." We proceeded with testing the spatial dependence as part of the results in 5.1 (Lines 489-495).
"The Lagrange multiplier test presented in table 3 is used to diagnose the type of spatial dependence that governs our data generation process among the endogenous effects, i.e. spatial lag of the dependent variable (ρ ̸ = 0) and the correlated effects or the spatial autocorrelation of the disturbance term (λ ̸ = 0).This test suggests rejecting all the specifications that allow spatial autocorrelation in the disturbance term.Therefore, we avoid estimating the SARAR, the SEM, and the generalized nested Manski spatial model.In the following, we estimate the SAR as it fits with our data generation process." -Step 3.This comment from Reviewer 1 brought us to confirm the first two steps using the Akaike information criterion (AIC) and the Bayes' information criterion (BIC) in lines 496-501.
"Comparing the Akaike information criterion (AIC) and Bayes' information criterion (BIC), we confirm the SAR model rather than the SDM and SEM models, cf.Appendix B.3.Indeed, the test displays lower BIC and AIC estimates for SAR compare to SEM and SDM.The relative amount of information lost by the SAR model is then lower than the amount lost by SEM and SDM models.We then prefer the SAR model with lower AIC and BIC." 3 Reviewer#2

• General Comment
General comment 1: The discussion needs to be supported with relevant related literature which the authors are yet to do with regards to the paper.
Answer to General Comment 1: This general comment is the same with comment n°14.This is addressed.See lines 720-735 and lines 765-769.
• Specific comments * Specific Comment 1: I suggest that the authors perform a through editorial work on this paper.There are several disjointed sentences and the authors needs to recast some areas for clarity.* Specific Comment 3:Cocoa is a tree crop and not a livelihood strategy but its production/cultivation.See line 3(Abstract), lines 277 and 280.I suggest it should read cocoa production.
Answer 3: This is addressed in the document.We now refer to cocoa production as part of livelihoods strategies, and not cocoa that is a three, see line 3(Abstract), lines 296 and 299.
* Specific Comment 4: The following sentences in the introduction section (line 2-3; 22-25,) should be referenced.As data quoted is not original to the authors as presented.
* Specific Comment 5: Line 33 the sentence "In the Congo Basin, Cameroon, . . . . . .."From the paper, Cameroon is among the countries in the Congo Basin.The authors will need to relook at the construction of the sentence again.
Answer 5: This is addressed.The sentence is rephrased.Please see lines 36-39.

Answer 1 :Answer 2 :
This comment is addressed.See the revised manuscript.* Specific Comment 2: I suggest that the title should read; Cocoa, livelihoods and deforestation within the Tridom landscape in the Congo Basin: A spatial analysis.Given the center focus and the study area is Tridom landscape which is in the Congo Basin.The title has been change from "Cocoa, livelihoods and deforestation in the Congo Basin: A spatial analysis" to "Cocoa, livelihoods and deforestation within the Tridom landscape in the Congo Basin: A spatial analysis".

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Requirement 4: We note that Figure2in your submission contains map images that may be copyrighted.For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth).For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.Comment: On the justification of the use of a spatial autoregressive model (SAR): why is a SAR model preferred to Spatial Durbin Model (SDM) or Spatial Error-correction Model (SEM)?Bayesian and AIC information criteria could be applied in order to confirm that SAR is the best method.
Answer: We have addressed this at page 21 and we hope we have done proper attribution, given the published by World Resources Institute (www.wri.org) in 2014, and are also available on the Forest Atlas platform and Global Forest Watch.Cameroon Forest Atlas, the Ministry of Forest and Fauna/World Resources Institute accessed on (10/03/2023), https://cmr.forest-atlas.org/.Republic of Congo Forest Atlas, Ministry of Forest Economy in the Republic of Congo /World Resources Institute was accessed on (10/03/2023), https://cog.forest-atlas.org/.Gabon Forest Atlas, Ministry of Water, forests, the Sea, and the Environment, in charge of the climate plan and the land use plan /World Resources Institute, accessed on (10/03/2023), https: //gab.forest-atlas.org/pages/mapsDeforestation: Data collected in the field in Gabon and Cameroun during the fieldwork carried out by the Authors in 2013 -2014.A total of 526 plots were tracked with a Global Positioning System (GPS) to have the real area.